# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved

# pyre-unsafe
"""
Transformer decoder.
Inspired from Pytorch's version, adds the pre-norm variant
"""

import math
from functools import partial
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch
import torch.nn.functional as torchF
from sam3.sam.rope import apply_rotary_enc, apply_rotary_enc_real, compute_axial_cis
from sam3.sam.transformer import RoPEAttention
from torch import nn, Tensor
from torch.nn.attention import sdpa_kernel, SDPBackend
from torchvision.ops.roi_align import RoIAlign

from .act_ckpt_utils import activation_ckpt_wrapper
from .box_ops import box_cxcywh_to_xyxy
from .model_misc import (
    gen_sineembed_for_position,
    get_activation_fn,
    get_clones,
    inverse_sigmoid,
    MLP,
)


class TransformerDecoderLayer(nn.Module):
    def __init__(
        self,
        activation: str,
        d_model: int,
        dim_feedforward: int,
        dropout: float,
        cross_attention: nn.Module,
        n_heads: int,
        use_text_cross_attention: bool = False,
    ):
        super().__init__()

        # cross attention
        self.cross_attn = cross_attention
        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
        self.norm1 = nn.LayerNorm(d_model)

        # cross attention text
        self.use_text_cross_attention = use_text_cross_attention
        if use_text_cross_attention:
            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
            self.catext_norm = nn.LayerNorm(d_model)

        # self attention
        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
        self.norm2 = nn.LayerNorm(d_model)

        # ffn
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.activation = get_activation_fn(activation)
        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
        self.norm3 = nn.LayerNorm(d_model)

    @staticmethod
    def with_pos_embed(tensor, pos):
        return tensor if pos is None else tensor + pos

    def forward_ffn(self, tgt):
        with torch.amp.autocast(device_type="cuda", enabled=False):
            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
        tgt = tgt + self.dropout4(tgt2)
        tgt = self.norm3(tgt)
        return tgt

    def forward(
        self,
        # for tgt
        tgt: Optional[Tensor],  # nq, bs, d_model
        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
        tgt_key_padding_mask: Optional[Tensor] = None,
        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
        memory_text: Optional[Tensor] = None,  # num_token, bs, d_model
        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
        # for memory
        memory: Optional[Tensor] = None,  # hw, bs, d_model
        memory_key_padding_mask: Optional[Tensor] = None,
        memory_level_start_index: Optional[Tensor] = None,  # num_levels
        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
        memory_pos: Optional[Tensor] = None,  # pos for memory
        # sa
        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
        # dac
        dac=False,
        dac_use_selfatt_ln=True,
        presence_token=None,
        # skip inside deformable attn
        identity=0.0,
        **kwargs,  # additional kwargs for compatibility
    ):
        """
        Input:
            - tgt/tgt_query_pos: nq, bs, d_model
            -
        """
        # self attention
        if self.self_attn is not None:
            if dac:
                # we only apply self attention to the first half of the queries
                assert tgt.shape[0] % 2 == 0
                num_o2o_queries = tgt.shape[0] // 2
                tgt_o2o = tgt[:num_o2o_queries]
                tgt_query_pos_o2o = tgt_query_pos[:num_o2o_queries]
                tgt_o2m = tgt[num_o2o_queries:]
            else:
                tgt_o2o = tgt
                tgt_query_pos_o2o = tgt_query_pos

            if presence_token is not None:
                tgt_o2o = torch.cat([presence_token, tgt_o2o], dim=0)
                tgt_query_pos_o2o = torch.cat(
                    [torch.zeros_like(presence_token), tgt_query_pos_o2o], dim=0
                )
                tgt_query_pos = torch.cat(
                    [torch.zeros_like(presence_token), tgt_query_pos], dim=0
                )

            q = k = self.with_pos_embed(tgt_o2o, tgt_query_pos_o2o)
            tgt2 = self.self_attn(q, k, tgt_o2o, attn_mask=self_attn_mask)[0]
            tgt_o2o = tgt_o2o + self.dropout2(tgt2)
            if dac:
                if not dac_use_selfatt_ln:
                    tgt_o2o = self.norm2(tgt_o2o)
                tgt = torch.cat((tgt_o2o, tgt_o2m), dim=0)  # Recombine
                if dac_use_selfatt_ln:
                    tgt = self.norm2(tgt)
            else:
                tgt = tgt_o2o
                tgt = self.norm2(tgt)

        if self.use_text_cross_attention:
            tgt2 = self.ca_text(
                self.with_pos_embed(tgt, tgt_query_pos),
                memory_text,
                memory_text,
                key_padding_mask=text_attention_mask,
            )[0]
            tgt = tgt + self.catext_dropout(tgt2)
            tgt = self.catext_norm(tgt)

        if presence_token is not None:
            presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :])
            cross_attn_mask = torch.cat(
                [presence_token_mask, cross_attn_mask], dim=1
            )  # (bs*nheads, 1+nq, hw)

        # Cross attention to image
        tgt2 = self.cross_attn(
            query=self.with_pos_embed(tgt, tgt_query_pos),
            key=self.with_pos_embed(memory, memory_pos),
            value=memory,
            attn_mask=cross_attn_mask,
            key_padding_mask=(
                memory_key_padding_mask.transpose(0, 1)
                if memory_key_padding_mask is not None
                else None
            ),
        )[0]

        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)

        # ffn
        tgt = self.forward_ffn(tgt)

        presence_token_out = None
        if presence_token is not None:
            presence_token_out = tgt[:1]
            tgt = tgt[1:]

        return tgt, presence_token_out


class TransformerDecoder(nn.Module):
    def __init__(
        self,
        d_model: int,
        frozen: bool,
        interaction_layer,
        layer,
        num_layers: int,
        num_queries: int,
        return_intermediate: bool,
        box_refine: bool = False,
        num_o2m_queries: int = 0,
        dac: bool = False,
        boxRPB: str = "none",
        # Experimental: An object query for SAM 2 tasks
        instance_query: bool = False,
        # Defines the number of additional instance queries,
        # 1 or 4 are the most likely for single vs multi mask support
        num_instances: int = 1,  # Irrelevant if instance_query is False
        dac_use_selfatt_ln: bool = True,
        use_act_checkpoint: bool = False,
        compile_mode=None,
        presence_token: bool = False,
        clamp_presence_logits: bool = True,
        clamp_presence_logit_max_val: float = 10.0,
        use_normed_output_consistently: bool = True,
        separate_box_head_instance: bool = False,
        separate_norm_instance: bool = False,
        resolution: Optional[int] = None,
        stride: Optional[int] = None,
    ):
        super().__init__()
        self.d_model = d_model
        self.layers = get_clones(layer, num_layers)
        self.fine_layers = (
            get_clones(interaction_layer, num_layers)
            if interaction_layer is not None
            else [None] * num_layers
        )
        self.num_layers = num_layers
        self.num_queries = num_queries
        self.dac = dac
        if dac:
            self.num_o2m_queries = num_queries
            tot_num_queries = num_queries
        else:
            self.num_o2m_queries = num_o2m_queries
            tot_num_queries = num_queries + num_o2m_queries
        self.norm = nn.LayerNorm(d_model)
        self.return_intermediate = return_intermediate
        self.bbox_embed = MLP(d_model, d_model, 4, 3)
        self.query_embed = nn.Embedding(tot_num_queries, d_model)
        self.instance_query_embed = None
        self.instance_query_reference_points = None
        self.use_instance_query = instance_query
        self.num_instances = num_instances
        self.use_normed_output_consistently = use_normed_output_consistently

        self.instance_norm = nn.LayerNorm(d_model) if separate_norm_instance else None
        self.instance_bbox_embed = None
        if separate_box_head_instance:
            self.instance_bbox_embed = MLP(d_model, d_model, 4, 3)
        if instance_query:
            self.instance_query_embed = nn.Embedding(num_instances, d_model)
        self.box_refine = box_refine
        if box_refine:
            nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
            nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)

            self.reference_points = nn.Embedding(num_queries, 4)
            if instance_query:
                self.instance_reference_points = nn.Embedding(num_instances, 4)

        assert boxRPB in ["none", "log", "linear", "both"]
        self.boxRPB = boxRPB
        if boxRPB != "none":
            try:
                nheads = self.layers[0].cross_attn_image.num_heads
            except AttributeError:
                nheads = self.layers[0].cross_attn.num_heads

            n_input = 4 if boxRPB == "both" else 2
            self.boxRPB_embed_x = MLP(n_input, d_model, nheads, 2)
            self.boxRPB_embed_y = MLP(n_input, d_model, nheads, 2)
            self.compilable_cord_cache = None
            self.compilable_stored_size = None
            self.coord_cache = {}

            if resolution is not None and stride is not None:
                feat_size = resolution // stride
                coords_h, coords_w = self._get_coords(
                    feat_size, feat_size, device="cuda"
                )
                self.compilable_cord_cache = (coords_h, coords_w)
                self.compilable_stored_size = (feat_size, feat_size)

        self.roi_pooler = (
            RoIAlign(output_size=7, spatial_scale=1, sampling_ratio=-1, aligned=True)
            if interaction_layer is not None
            else None
        )
        if frozen:
            for p in self.parameters():
                p.requires_grad_(False)

        self.presence_token = None
        self.clamp_presence_logits = clamp_presence_logits
        self.clamp_presence_logit_max_val = clamp_presence_logit_max_val
        if presence_token:
            self.presence_token = nn.Embedding(1, d_model)
            self.presence_token_head = MLP(d_model, d_model, 1, 3)
            self.presence_token_out_norm = nn.LayerNorm(d_model)

        self.ref_point_head = MLP(2 * self.d_model, self.d_model, self.d_model, 2)
        self.dac_use_selfatt_ln = dac_use_selfatt_ln
        self.use_act_checkpoint = use_act_checkpoint

        nn.init.normal_(self.query_embed.weight.data)
        if self.instance_query_embed is not None:
            nn.init.normal_(self.instance_query_embed.weight.data)

        assert self.roi_pooler is None
        assert self.return_intermediate, "support return_intermediate only"
        assert self.box_refine, "support box refine only"

        self.compile_mode = compile_mode
        self.compiled = False
        # We defer compilation till after the first forward, to first warm-up the boxRPB cache

        # assign layer index to each layer so that some layers can decide what to do
        # based on which layer index they are (e.g. cross attention to memory bank only
        # in selected layers)
        for layer_idx, layer in enumerate(self.layers):
            layer.layer_idx = layer_idx

    @staticmethod
    def _get_coords(H, W, device):
        coords_h = torch.arange(0, H, device=device, dtype=torch.float32) / H
        coords_w = torch.arange(0, W, device=device, dtype=torch.float32) / W
        return coords_h, coords_w

    def _get_rpb_matrix(self, reference_boxes, feat_size):
        H, W = feat_size
        boxes_xyxy = box_cxcywh_to_xyxy(reference_boxes).transpose(0, 1)
        bs, num_queries, _ = boxes_xyxy.shape
        if self.compilable_cord_cache is None:
            self.compilable_cord_cache = self._get_coords(H, W, reference_boxes.device)
            self.compilable_stored_size = (H, W)

        if torch.compiler.is_dynamo_compiling() or self.compilable_stored_size == (
            H,
            W,
        ):
            # good, hitting the cache, will be compilable
            coords_h, coords_w = self.compilable_cord_cache
        else:
            # cache miss, will create compilation issue
            # In case we're not compiling, we'll still rely on the dict-based cache
            if feat_size not in self.coord_cache:
                self.coord_cache[feat_size] = self._get_coords(
                    H, W, reference_boxes.device
                )
            coords_h, coords_w = self.coord_cache[feat_size]

            assert coords_h.shape == (H,)
            assert coords_w.shape == (W,)

        deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2]
        deltas_y = deltas_y.view(bs, num_queries, -1, 2)
        deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2]
        deltas_x = deltas_x.view(bs, num_queries, -1, 2)

        if self.boxRPB in ["log", "both"]:
            deltas_x_log = deltas_x * 8  # normalize to -8, 8
            deltas_x_log = (
                torch.sign(deltas_x_log)
                * torch.log2(torch.abs(deltas_x_log) + 1.0)
                / np.log2(8)
            )

            deltas_y_log = deltas_y * 8  # normalize to -8, 8
            deltas_y_log = (
                torch.sign(deltas_y_log)
                * torch.log2(torch.abs(deltas_y_log) + 1.0)
                / np.log2(8)
            )
            if self.boxRPB == "log":
                deltas_x = deltas_x_log
                deltas_y = deltas_y_log
            else:
                deltas_x = torch.cat([deltas_x, deltas_x_log], dim=-1)
                deltas_y = torch.cat([deltas_y, deltas_y_log], dim=-1)

        if self.training:
            assert self.use_act_checkpoint, "activation ckpt not enabled in decoder"
        deltas_x = activation_ckpt_wrapper(self.boxRPB_embed_x)(
            x=deltas_x,
            act_ckpt_enable=self.training and self.use_act_checkpoint,
        )  # bs, num_queries, W, n_heads
        deltas_y = activation_ckpt_wrapper(self.boxRPB_embed_y)(
            x=deltas_y,
            act_ckpt_enable=self.training and self.use_act_checkpoint,
        )  # bs, num_queries, H, n_heads

        if not torch.compiler.is_dynamo_compiling():
            assert deltas_x.shape[:3] == (bs, num_queries, W)
            assert deltas_y.shape[:3] == (bs, num_queries, H)

        B = deltas_y.unsqueeze(3) + deltas_x.unsqueeze(
            2
        )  # bs, num_queries, H, W, n_heads
        if not torch.compiler.is_dynamo_compiling():
            assert B.shape[:4] == (bs, num_queries, H, W)
        B = B.flatten(2, 3)  # bs, num_queries, H*W, n_heads
        B = B.permute(0, 3, 1, 2)  # bs, n_heads, num_queries, H*W
        B = B.contiguous()  # memeff attn likes ordered strides
        if not torch.compiler.is_dynamo_compiling():
            assert B.shape[2:] == (num_queries, H * W)
        return B

    def forward(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        reference_boxes: Optional[Tensor] = None,  # num_queries, bs, 4
        # for memory
        level_start_index: Optional[Tensor] = None,  # num_levels
        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
        valid_ratios: Optional[Tensor] = None,
        # for text
        memory_text: Optional[Tensor] = None,
        text_attention_mask: Optional[Tensor] = None,
        # if `apply_dac` is None, it will default to `self.dac`
        apply_dac: Optional[bool] = None,
        is_instance_prompt=False,
        decoder_extra_kwargs: Optional[Dict] = None,
        # ROI memory bank
        obj_roi_memory_feat=None,
        obj_roi_memory_mask=None,
        box_head_trk=None,
    ):
        """
        Input:
            - tgt: nq, bs, d_model
            - memory: \\sum{hw}, bs, d_model
            - pos: \\sum{hw}, bs, d_model
            - reference_boxes: nq, bs, 4 (after sigmoid)
            - valid_ratios/spatial_shapes: bs, nlevel, 2
        """
        if memory_mask is not None:
            assert self.boxRPB == "none", (
                "inputting a memory_mask in the presence of boxRPB is unexpected/not implemented"
            )

        apply_dac = apply_dac if apply_dac is not None else self.dac
        if apply_dac:
            assert (tgt.shape[0] == self.num_queries) or (
                self.use_instance_query
                and (tgt.shape[0] == self.instance_query_embed.num_embeddings)
            )

            tgt = tgt.repeat(2, 1, 1)
            # note that we don't tile tgt_mask, since DAC doesn't
            # use self-attention in o2m queries
            if reference_boxes is not None:
                assert (reference_boxes.shape[0] == self.num_queries) or (
                    self.use_instance_query
                    and (
                        reference_boxes.shape[0]
                        == self.instance_query_embed.num_embeddings
                    )
                )
                reference_boxes = reference_boxes.repeat(2, 1, 1)

        bs = tgt.shape[1]
        intermediate = []
        intermediate_presence_logits = []
        presence_feats = None

        if self.box_refine:
            if reference_boxes is None:
                # In this case, we're in a one-stage model, so we generate the reference boxes
                reference_boxes = self.reference_points.weight.unsqueeze(1)
                reference_boxes = (
                    reference_boxes.repeat(2, bs, 1)
                    if apply_dac
                    else reference_boxes.repeat(1, bs, 1)
                )
                reference_boxes = reference_boxes.sigmoid()
            intermediate_ref_boxes = [reference_boxes]
        else:
            reference_boxes = None
            intermediate_ref_boxes = None

        output = tgt
        presence_out = None
        if self.presence_token is not None and is_instance_prompt is False:
            # expand to batch dim
            presence_out = self.presence_token.weight[None].expand(1, bs, -1)

        box_head = self.bbox_embed
        if is_instance_prompt and self.instance_bbox_embed is not None:
            box_head = self.instance_bbox_embed

        out_norm = self.norm
        if is_instance_prompt and self.instance_norm is not None:
            out_norm = self.instance_norm

        for layer_idx, layer in enumerate(self.layers):
            reference_points_input = (
                reference_boxes[:, :, None]
                * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
            )  # nq, bs, nlevel, 4

            query_sine_embed = gen_sineembed_for_position(
                reference_points_input[:, :, 0, :], self.d_model
            )  # nq, bs, d_model*2

            # conditional query
            query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, d_model

            if self.boxRPB != "none" and reference_boxes is not None:
                assert spatial_shapes.shape[0] == 1, (
                    "only single scale support implemented"
                )
                memory_mask = self._get_rpb_matrix(
                    reference_boxes,
                    (spatial_shapes[0, 0], spatial_shapes[0, 1]),
                )
                memory_mask = memory_mask.flatten(0, 1)  # (bs*n_heads, nq, H*W)
            if self.training:
                assert self.use_act_checkpoint, (
                    "Activation checkpointing not enabled in the decoder"
                )
            output, presence_out = activation_ckpt_wrapper(layer)(
                tgt=output,
                tgt_query_pos=query_pos,
                tgt_query_sine_embed=query_sine_embed,
                tgt_key_padding_mask=tgt_key_padding_mask,
                tgt_reference_points=reference_points_input,
                memory_text=memory_text,
                text_attention_mask=text_attention_mask,
                memory=memory,
                memory_key_padding_mask=memory_key_padding_mask,
                memory_level_start_index=level_start_index,
                memory_spatial_shapes=spatial_shapes,
                memory_pos=pos,
                self_attn_mask=tgt_mask,
                cross_attn_mask=memory_mask,
                dac=apply_dac,
                dac_use_selfatt_ln=self.dac_use_selfatt_ln,
                presence_token=presence_out,
                **(decoder_extra_kwargs or {}),
                act_ckpt_enable=self.training and self.use_act_checkpoint,
                # ROI memory bank
                obj_roi_memory_feat=obj_roi_memory_feat,
                obj_roi_memory_mask=obj_roi_memory_mask,
            )

            # iter update
            if self.box_refine:
                reference_before_sigmoid = inverse_sigmoid(reference_boxes)
                if box_head_trk is None:
                    # delta_unsig = self.bbox_embed(output)
                    if not self.use_normed_output_consistently:
                        delta_unsig = box_head(output)
                    else:
                        delta_unsig = box_head(out_norm(output))
                else:
                    # box_head_trk use a separate box head for tracking queries
                    Q_det = decoder_extra_kwargs["Q_det"]
                    assert output.size(0) >= Q_det
                    delta_unsig_det = self.bbox_embed(output[:Q_det])
                    delta_unsig_trk = box_head_trk(output[Q_det:])
                    delta_unsig = torch.cat([delta_unsig_det, delta_unsig_trk], dim=0)
                outputs_unsig = delta_unsig + reference_before_sigmoid
                new_reference_points = outputs_unsig.sigmoid()

                reference_boxes = new_reference_points.detach()
                if layer_idx != self.num_layers - 1:
                    intermediate_ref_boxes.append(new_reference_points)
            else:
                raise NotImplementedError("not implemented yet")

            intermediate.append(out_norm(output))
            if self.presence_token is not None and is_instance_prompt is False:
                # norm, mlp head
                intermediate_layer_presence_logits = self.presence_token_head(
                    self.presence_token_out_norm(presence_out)
                ).squeeze(-1)

                # clamp to mitigate numerical issues
                if self.clamp_presence_logits:
                    intermediate_layer_presence_logits.clamp(
                        min=-self.clamp_presence_logit_max_val,
                        max=self.clamp_presence_logit_max_val,
                    )

                intermediate_presence_logits.append(intermediate_layer_presence_logits)
                presence_feats = presence_out.clone()

        if not self.compiled and self.compile_mode is not None:
            self.forward = torch.compile(
                self.forward, mode=self.compile_mode, fullgraph=True
            )
            self.compiled = True

        return (
            torch.stack(intermediate),
            torch.stack(intermediate_ref_boxes),
            (
                torch.stack(intermediate_presence_logits)
                if self.presence_token is not None and is_instance_prompt is False
                else None
            ),
            presence_feats,
        )


class TransformerEncoderCrossAttention(nn.Module):
    def __init__(
        self,
        d_model: int,
        frozen: bool,
        pos_enc_at_input: bool,
        layer,
        num_layers: int,
        use_act_checkpoint: bool = False,
        batch_first: bool = False,  # Do layers expect batch first input?
        # which layers to exclude cross attention? default: None, means all
        # layers use cross attention
        remove_cross_attention_layers: Optional[list] = None,
    ):
        super().__init__()
        self.d_model = d_model
        self.layers = get_clones(layer, num_layers)
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(d_model)
        self.pos_enc_at_input = pos_enc_at_input
        self.use_act_checkpoint = use_act_checkpoint

        if frozen:
            for p in self.parameters():
                p.requires_grad_(False)

        self.batch_first = batch_first

        # remove cross attention layers if specified
        self.remove_cross_attention_layers = [False] * self.num_layers
        if remove_cross_attention_layers is not None:
            for i in remove_cross_attention_layers:
                self.remove_cross_attention_layers[i] = True
        assert len(self.remove_cross_attention_layers) == len(self.layers)

        for i, remove_cross_attention in enumerate(self.remove_cross_attention_layers):
            if remove_cross_attention:
                self.layers[i].cross_attn_image = None
                self.layers[i].norm2 = None
                self.layers[i].dropout2 = None

    def forward(
        self,
        src,  # self-attention inputs
        prompt,  # cross-attention inputs
        src_mask: Optional[Tensor] = None,  # att.mask for self-attention inputs
        prompt_mask: Optional[Tensor] = None,  # att.mask for cross-attention inputs
        src_key_padding_mask: Optional[Tensor] = None,
        prompt_key_padding_mask: Optional[Tensor] = None,
        src_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
        prompt_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
        feat_sizes: Optional[list] = None,
        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
    ):
        if isinstance(src, list):
            assert isinstance(src_key_padding_mask, list) and isinstance(src_pos, list)
            assert len(src) == len(src_key_padding_mask) == len(src_pos) == 1
            src, src_key_padding_mask, src_pos = (
                src[0],
                src_key_padding_mask[0],
                src_pos[0],
            )

        assert src.shape[1] == prompt.shape[1], (
            "Batch size must be the same for src and prompt"
        )

        output = src

        if self.pos_enc_at_input and src_pos is not None:
            output = output + 0.1 * src_pos

        if self.batch_first:
            # Convert to batch first
            output = output.transpose(0, 1)
            src_pos = src_pos.transpose(0, 1)
            prompt = prompt.transpose(0, 1)
            prompt_pos = prompt_pos.transpose(0, 1)

        for layer in self.layers:
            kwds = {}
            if isinstance(layer.cross_attn_image, RoPEAttention):
                kwds = {"num_k_exclude_rope": num_obj_ptr_tokens}

            output = activation_ckpt_wrapper(layer)(
                tgt=output,
                memory=prompt,
                tgt_mask=src_mask,
                memory_mask=prompt_mask,
                tgt_key_padding_mask=src_key_padding_mask,
                memory_key_padding_mask=prompt_key_padding_mask,
                pos=prompt_pos,
                query_pos=src_pos,
                dac=False,
                attn_bias=None,
                act_ckpt_enable=self.training and self.use_act_checkpoint,
                **kwds,
            )
            normed_output = self.norm(output)

        if self.batch_first:
            # Convert back to seq first
            normed_output = normed_output.transpose(0, 1)
            src_pos = src_pos.transpose(0, 1)

        return {
            "memory": normed_output,
            "pos_embed": src_pos,
            "padding_mask": src_key_padding_mask,
        }


class TransformerDecoderLayerv1(nn.Module):
    def __init__(
        self,
        activation: str,
        cross_attention: nn.Module,
        d_model: int,
        dim_feedforward: int,
        dropout: float,
        pos_enc_at_attn: bool,
        pos_enc_at_cross_attn_keys: bool,
        pos_enc_at_cross_attn_queries: bool,
        pre_norm: bool,
        self_attention: nn.Module,
    ):
        super().__init__()
        self.d_model = d_model
        self.dim_feedforward = dim_feedforward
        self.dropout_value = dropout
        self.self_attn = self_attention
        self.cross_attn_image = cross_attention

        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation_str = activation
        self.activation = get_activation_fn(activation)
        self.pre_norm = pre_norm

        self.pos_enc_at_attn = pos_enc_at_attn
        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys

    def forward_post(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        **kwargs,
    ):
        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt

        # Self attention
        tgt2 = self.self_attn(
            q,
            k,
            value=tgt,
            attn_mask=tgt_mask,
            key_padding_mask=tgt_key_padding_mask,
        )[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)

        # Cross attention to image
        tgt2 = self.cross_attn_image(
            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,
            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
            value=memory,
            attn_mask=memory_mask,
            key_padding_mask=memory_key_padding_mask,
        )[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)

        # FFN
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
        tgt = tgt + self.dropout3(tgt2)
        tgt = self.norm3(tgt)
        return tgt

    def forward_pre(
        self,
        tgt,
        memory,
        dac: bool = False,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        attn_bias: Optional[Tensor] = None,
        **kwargs,
    ):
        if dac:
            # we only apply self attention to the first half of the queries
            assert tgt.shape[0] % 2 == 0
            other_tgt = tgt[tgt.shape[0] // 2 :]
            tgt = tgt[: tgt.shape[0] // 2]
        tgt2 = self.norm1(tgt)
        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
        tgt2 = self.self_attn(
            q,
            k,
            value=tgt2,
            attn_mask=tgt_mask,
            key_padding_mask=tgt_key_padding_mask,
        )[0]
        tgt = tgt + self.dropout1(tgt2)
        if dac:
            # Recombine
            tgt = torch.cat((tgt, other_tgt), dim=0)
        tgt2 = self.norm2(tgt)
        tgt2 = self.cross_attn_image(
            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
            value=memory,
            attn_mask=memory_mask,
            key_padding_mask=memory_key_padding_mask,
            attn_bias=attn_bias,
        )[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt

    def forward(
        self,
        tgt,
        memory,
        dac: bool = False,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        attn_bias: Optional[Tensor] = None,
        **kwds: Any,
    ) -> torch.Tensor:
        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post
        return fwd_fn(
            tgt,
            memory,
            dac=dac,
            tgt_mask=tgt_mask,
            memory_mask=memory_mask,
            tgt_key_padding_mask=tgt_key_padding_mask,
            memory_key_padding_mask=memory_key_padding_mask,
            pos=pos,
            query_pos=query_pos,
            attn_bias=attn_bias,
            **kwds,
        )


class TransformerDecoderLayerv2(TransformerDecoderLayerv1):
    def __init__(self, cross_attention_first=False, *args: Any, **kwds: Any):
        super().__init__(*args, **kwds)
        self.cross_attention_first = cross_attention_first

    def _forward_sa(self, tgt, query_pos):
        # Self-Attention
        tgt2 = self.norm1(tgt)
        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
        tgt2 = self.self_attn(q, k, v=tgt2)
        tgt = tgt + self.dropout1(tgt2)
        return tgt

    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
        if self.cross_attn_image is None:
            return tgt

        kwds = {}
        if num_k_exclude_rope > 0:
            assert isinstance(self.cross_attn_image, RoPEAttention)
            kwds = {"num_k_exclude_rope": num_k_exclude_rope}

        # Cross-Attention
        tgt2 = self.norm2(tgt)
        tgt2 = self.cross_attn_image(
            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
            v=memory,
            **kwds,
        )
        tgt = tgt + self.dropout2(tgt2)
        return tgt

    def forward_pre(
        self,
        tgt,
        memory,
        dac: bool,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        attn_bias: Optional[Tensor] = None,
        num_k_exclude_rope: int = 0,
    ):
        assert dac is False
        assert tgt_mask is None
        assert memory_mask is None
        assert tgt_key_padding_mask is None
        assert memory_key_padding_mask is None
        assert attn_bias is None

        if self.cross_attention_first:
            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
            tgt = self._forward_sa(tgt, query_pos)
        else:
            tgt = self._forward_sa(tgt, query_pos)
            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)

        # MLP
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt

    def forward(self, *args: Any, **kwds: Any) -> torch.Tensor:
        if self.pre_norm:
            return self.forward_pre(*args, **kwds)
        raise NotImplementedError


def functional_attention(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    *,
    dropout: float,
    num_heads: int,
    num_k_exclude_rope: int = 0,
    freqs_cis: Optional[Tensor] = None,
    freqs_cis_real: Optional[Tensor] = None,
    freqs_cis_imag: Optional[Tensor] = None,
    use_fa3: bool = False,
    use_rope_real: bool = False,
    rope_k_repeat: bool,
) -> Union[Tensor, tuple[Tensor, Tensor]]:
    b, n, cq = q.shape
    _, m, ck = k.shape
    _, _, cv = v.shape
    if b > 1:
        assert k.shape[0] == v.shape[0] == b
    else:
        # broadcast-able
        assert k.shape[0] == b == 1, f"{q.shape=} {k.shape=} {v.shape=}"
    assert v.shape[1] == m

    q = q.reshape(b, n, num_heads, cq // num_heads).transpose(1, 2)
    k = k.reshape(b, m, num_heads, ck // num_heads).transpose(1, 2)
    v = v.reshape(v.shape[0], m, num_heads, cv // num_heads).transpose(1, 2)

    if freqs_cis is not None:
        num_k_rope = k.size(-2) - num_k_exclude_rope
        if use_rope_real:
            q, k[:, :, :num_k_rope] = apply_rotary_enc_real(
                q,
                k[:, :, :num_k_rope],
                freqs_cis_real=freqs_cis_real,
                freqs_cis_imag=freqs_cis_imag,
                repeat_freqs_k=rope_k_repeat,
            )
        else:
            q, k[:, :, :num_k_rope] = apply_rotary_enc(
                q,
                k[:, :, :num_k_rope],
                freqs_cis,
                repeat_freqs_k=rope_k_repeat,
            )

    if use_fa3:
        from sam3.perflib.fa3 import flash_attn_func

        assert dropout == 0.0
        out = flash_attn_func(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2))
    else:
        with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
            out = torchF.scaled_dot_product_attention(q, k, v, dropout_p=dropout)
        out = out.transpose(1, 2)  #  B * n * n_heads * (cv // num_heads)

    out = out.reshape(b, n, cv)
    return out


class SimpleRoPEAttention(nn.Module):
    """
    Attention with rotary position encoding.
    This class is "simple" because it does not perform q/k/v/out projections.
    """

    def __init__(
        self,
        d_model: int,
        num_heads: int,
        dropout_p: float,
        rope_theta=10000.0,
        # whether to repeat q rope to match k length
        # this is needed for cross-attention to memories
        rope_k_repeat=False,
        feat_sizes=(64, 64),  # [w, h] for stride 16 feats at 1024 resolution
        use_fa3: bool = False,
        use_rope_real: bool = False,
    ):
        super().__init__()

        self.num_heads = num_heads
        self.dropout_p = dropout_p
        self.compute_cis = partial(
            compute_axial_cis, dim=d_model // num_heads, theta=rope_theta
        )
        device = torch.device("cuda") if torch.cuda.is_available() else None
        self.freqs_cis = self.compute_cis(
            end_x=feat_sizes[0], end_y=feat_sizes[1], device=device
        )

        self.use_fa3 = use_fa3
        self.use_rope_real = use_rope_real
        if self.use_rope_real:
            self.freqs_cis_real = self.freqs_cis.real
            self.freqs_cis_imag = self.freqs_cis.imag
        self.rope_k_repeat = rope_k_repeat

    def forward(
        self,
        q: Tensor,
        k: Tensor,
        v: Tensor,
        num_k_exclude_rope: int = 0,
    ) -> Union[Tensor, tuple[Tensor, Tensor]]:
        # Apply rotary position encoding
        w = h = math.sqrt(q.shape[-2])
        self.freqs_cis = self.freqs_cis.to(q.device)
        if self.freqs_cis.shape[0] != q.shape[-2]:
            self.freqs_cis = self.compute_cis(end_x=w, end_y=h, device=q.device)
            if self.use_rope_real:
                self.freqs_cis_real = self.freqs_cis.real
                self.freqs_cis_imag = self.freqs_cis.imag
        if q.shape[-2] != k.shape[-2]:
            assert self.rope_k_repeat

        dropout_p = self.dropout_p if self.training else 0.0
        out = functional_attention(
            q,
            k,
            v,
            dropout=dropout_p,
            num_heads=self.num_heads,
            num_k_exclude_rope=num_k_exclude_rope,
            freqs_cis=self.freqs_cis,
            freqs_cis_real=self.freqs_cis_real if self.use_rope_real else None,
            freqs_cis_imag=self.freqs_cis_imag if self.use_rope_real else None,
            use_fa3=self.use_fa3,
            use_rope_real=self.use_rope_real,
            rope_k_repeat=self.rope_k_repeat,
        )

        return out


class DecoupledTransformerDecoderLayerv2(nn.Module):
    def __init__(
        self,
        *,
        activation: str,
        d_model: int,
        num_heads: int,
        dim_feedforward: int,
        dropout: float,
        pos_enc_at_attn: bool,
        pos_enc_at_cross_attn_keys: bool,
        pos_enc_at_cross_attn_queries: bool,
        pre_norm: bool,
        cross_attention_first: bool = False,
        self_attention_rope: SimpleRoPEAttention,
        cross_attention_rope: SimpleRoPEAttention,
    ):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.dim_feedforward = dim_feedforward
        self.dropout_value = dropout

        self.self_attn_q_proj = nn.Linear(d_model, d_model)
        self.self_attn_k_proj = nn.Linear(d_model, d_model)
        self.self_attn_v_proj = nn.Linear(d_model, d_model)
        self.self_attn_out_proj = nn.Linear(d_model, d_model)

        self.cross_attn_q_proj = nn.Linear(d_model, d_model)
        self.cross_attn_k_proj = nn.Linear(d_model, d_model)
        self.cross_attn_v_proj = nn.Linear(d_model, d_model)
        self.cross_attn_out_proj = nn.Linear(d_model, d_model)

        self.image_cross_attn_q_proj = nn.Linear(d_model, d_model)
        self.image_cross_attn_k_proj = nn.Linear(d_model, d_model)

        self.self_attention_rope = self_attention_rope
        self.cross_attention_rope = cross_attention_rope

        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation_str = activation
        self.activation = get_activation_fn(activation)
        self.pre_norm = pre_norm

        self.pos_enc_at_attn = pos_enc_at_attn
        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys

        self.cross_attention_first = cross_attention_first

    def _forward_sa(self, tgt, query_pos):
        # Self-Attention
        tgt2 = self.norm1(tgt)

        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2

        q = self.self_attn_q_proj(q)
        k = self.self_attn_k_proj(k)
        v = self.self_attn_v_proj(tgt2)
        out = self.self_attention_rope(q, k, v)
        tgt2 = self.self_attn_out_proj(out)

        tgt = tgt + self.dropout1(tgt2)
        return tgt

    def _forward_ca(
        self,
        *,
        image,
        tgt,
        memory_image,
        memory,
        query_pos,
        memory_image_pos,
        num_k_exclude_rope=0,
    ):
        kwds = {}
        if num_k_exclude_rope > 0:
            assert isinstance(self.cross_attention_rope, SimpleRoPEAttention)
            kwds = {"num_k_exclude_rope": num_k_exclude_rope}

        # Cross-Attention
        tgt2 = self.norm2(tgt)

        q = self.image_cross_attn_q_proj(image) + self.cross_attn_q_proj(tgt2)
        if self.pos_enc_at_cross_attn_queries:
            q = q + query_pos
        k = self.image_cross_attn_k_proj(memory_image) + self.cross_attn_k_proj(memory)
        if self.pos_enc_at_cross_attn_keys:
            k = k + memory_image_pos
        v = self.cross_attn_v_proj(memory)

        out = self.cross_attention_rope(q, k, v, **kwds)
        tgt2 = self.cross_attn_out_proj(out)

        tgt = tgt + self.dropout2(tgt2)
        return tgt

    def forward_pre(
        self,
        *,
        image,
        tgt,
        memory_image,
        memory,
        image_pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        memory_image_pos: Optional[Tensor] = None,
        memory_pos: Optional[Tensor] = None,
        num_k_exclude_rope: int = 0,
    ):
        if self.cross_attention_first:
            tgt = self._forward_ca(
                image=image,
                tgt=tgt,
                memory_image=memory_image,
                memory=memory,
                query_pos=query_pos,
                memory_image_pos=memory_image_pos,
                num_k_exclude_rope=num_k_exclude_rope,
            )
            tgt = self._forward_sa(tgt, query_pos)
        else:
            tgt = self._forward_sa(tgt, query_pos)
            tgt = self._forward_ca(
                image=image,
                tgt=tgt,
                memory_image=memory_image,
                memory=memory,
                query_pos=query_pos,
                memory_image_pos=memory_image_pos,
                num_k_exclude_rope=num_k_exclude_rope,
            )

        # MLP
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)

        return image, tgt

    def forward(self, *args: Any, **kwds: Any) -> torch.Tensor:
        if self.pre_norm:
            return self.forward_pre(*args, **kwds)
        raise NotImplementedError


class TransformerEncoderDecoupledCrossAttention(nn.Module):
    def __init__(
        self,
        d_model: int,
        frozen: bool,
        pos_enc_at_input: bool,
        layer,
        num_layers: int,
        use_act_checkpoint: bool = False,
        batch_first: bool = False,  # Do layers expect batch first input?
        use_image_in_output: bool = True,
    ):
        super().__init__()
        self.d_model = d_model
        self.layers = get_clones(layer, num_layers)
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(d_model)
        self.pos_enc_at_input = pos_enc_at_input
        self.use_act_checkpoint = use_act_checkpoint
        self.use_image_in_output = use_image_in_output

        if frozen:
            for p in self.parameters():
                p.requires_grad_(False)

        self.batch_first = batch_first

    def forward(
        self,
        image: Tensor,  # image features
        src: Tensor,  # self-attention inputs; object features
        memory_image: Tensor,  # cross-attention inputs; image features
        memory: Tensor,  # cross-attention inputs; object features
        image_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
        src_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
        memory_image_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
        memory_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
    ):
        assert src.shape[1] == memory.shape[1], (
            "Batch size must be the same for src and memory"
        )
        assert image.shape[1] == memory_image.shape[1], (
            "Batch size must be the same for image and memory_image"
        )

        output = src

        if self.pos_enc_at_input and src_pos is not None:
            output = output + 0.1 * src_pos

        if self.batch_first:
            # Convert to batch first
            output = output.transpose(0, 1)
            src_pos = src_pos.transpose(0, 1)
            image = image.transpose(0, 1)
            memory = memory.transpose(0, 1)
            memory_pos = memory_pos.transpose(0, 1)
            memory_image = memory_image.transpose(0, 1)
            memory_image_pos = memory_image_pos.transpose(0, 1)

        if memory_image.shape[1] != memory.shape[1]:
            # Pad memory_image with zeros, to accodmate object pointers
            assert (memory.shape[1] - memory_image.shape[1]) == num_obj_ptr_tokens, (
                f"{memory.shape[1]} - {memory_image.shape[1]} != {num_obj_ptr_tokens}"
            )
            memory_image = torch.cat(
                [
                    memory_image,
                    torch.zeros(
                        (memory_image.shape[0], num_obj_ptr_tokens)
                        + memory_image.shape[2:],
                        dtype=memory_image.dtype,
                        device=memory_image.device,
                    ),
                ],
                dim=1,
            )
            if memory_image_pos is not None:
                assert (
                    memory_pos.shape[1] - memory_image_pos.shape[1]
                ) == num_obj_ptr_tokens, (
                    f"{memory_pos.shape[1]} - {memory_image_pos.shape[1]} != {num_obj_ptr_tokens}"
                )
                # tpos is the same in the batch anyway; note that memory_image always has a batch size of 1
                memory_image_pos = torch.cat(
                    [
                        memory_image_pos,
                        memory_pos[0:1, -num_obj_ptr_tokens:],
                    ],
                    dim=1,
                )

        for layer in self.layers:
            image, output = activation_ckpt_wrapper(layer)(
                image=image,
                tgt=output,
                memory_image=memory_image,
                memory=memory,
                image_pos=image_pos,
                query_pos=src_pos,
                memory_image_pos=memory_image_pos,
                memory_pos=memory_pos,
                num_k_exclude_rope=num_obj_ptr_tokens,
                act_ckpt_enable=self.training and self.use_act_checkpoint,
            )

        if self.use_image_in_output:
            normed_output = self.norm(output + image)
        else:
            normed_output = self.norm(output)

        if self.batch_first:
            # Convert back to seq first
            normed_output = normed_output.transpose(0, 1)
            src_pos = src_pos.transpose(0, 1)

        return {
            "memory": normed_output,
            "pos_embed": src_pos,
        }
